Improving manufacturing yield is critical in the semiconductor industry. Because memory arrays have simple, regular structures, memory diagnostics and failure analysis methodologies are widely used to monitor manufacturing processes and to identify yield-limiting root causes. In a typical yield analysis process, memory failure bitmaps are generated based on memory diagnosis results. From the memory failure bitmaps, groups of failing bits (failure patterns) are extracted and classified according to geometric shapes of the failure patterns. Different groups of failure patterns usually correspond to different defect types and thus reveal the yield problems. This process reduces the need of expensive physical failure analysis. Examples showing the association between failure patterns in a memory failure bitmap and physical defect types can be found in a paper by Baltagi et al., “Embedded memory fail analysis for production yield enhancement,” in Advanced Semiconductor Manufacturing Conference (ASMC), 2011, 22nd Annual IEEE/SEMI, pp. 1-5, which is incorporated herein by reference.
The classification of failure patterns in a memory bitmap can be performed based on predefined rules. This approach is often referred to as rule-based classification or dictionary-based classification as a dictionary is usually employed to store the predefined rules. Another approach is based on machine learning such as artificial neural networks. In a neural network-based classification process, a neural network model is first established or trained with a set of training failure patterns. Then, feature vectors for failure patterns of interest are extracted. The feature extraction is mainly a data reduction process. Based on the extracted feature vectors, the neural network model assigns the failure patterns of interest to various nodes of the network (defect types).
The rule-based classification is accurate and fast, but cannot work with unknown failure patterns. While the artificial neural network-based classification can classify unknown failure patterns, it is challenging to achieve desired accuracy without resorting to expensive computing resources. A hybrid approach can improve the classification accuracy and/or the classification speed.